Scaffold-Hopped Compound Identification by Ligand-Based Approaches with a Prospective Affinity Test.

Scaffold-hopped (SH) compounds are bioactive compounds structurally different from known active compounds. Identifying SH compounds in the ligand-based approaches has been a central issue in medicinal chemistry, and various molecular representations of scaffold hopping have been proposed. However, appropriate representations for SH compound identification remain unclear. Herein, the ability of SH compound identification among several representations was fairly evaluated based on retrospective validation and prospective demonstration. In the retrospective validation, the combinations of two screening algorithms and four two- and three-dimensional molecular representations were compared using controlled data sets for the early identification of SH compounds. We found that the combination of the support vector machine and extended connectivity fingerprint with bond diameter 4 (SVM-ECFP4) and SVM and the rapid overlay of chemical structures (SVM-ROCS) showed a relatively high performance. The compounds that were highly ranked by SVM-ROCS did not share substructures with the active training compounds, while those ranked by SVM-ECFP4 were mostly recombinant. In the prospective demonstration, 93 SH compounds were prepared by screening the Namiki database using SVM-ROCS, targeting ABL1 inhibitors. The primary screening using surface plasmon resonance suggested five active compounds; however, in the competitive binding assays with adenosine triphosphate, no hits were found.

Global Analysis of Deep Learning Prediction Using Large-Scale In-House Kinome-Wide Profiling Data.

In drug discovery, the prediction of activity and absorption, distribution, metabolism, excretion, and toxicity parameters is one of the most important approaches in determining which compound to synthesize next. In recent years, prediction methods based on deep learning as well as non-deep learning approaches have been established, and a number of applications to drug discovery have been reported by various companies and organizations. In this research, we performed activity prediction using deep learning and non-deep learning methods on in-house assay data for several hundred kinases and compared and discussed the prediction results. We found that the prediction accuracy of the single-task graph neural network (GNN) model was generally lower than that of the non-deep learning model (LightGBM), but the multitask GNN model, which combined data from other kinases, comprehensively outperformed LightGBM. In addition, the extrapolative validity of the multitask model was verified by using it for prediction on known kinase ligands. We observed an overlap between characteristic protein-ligand interaction sites and the atoms that are important for prediction. By building appropriate models based on the conditions of the data set and analyzing the feature importance of the prediction results, a ligand-based prediction method may be used not only for activity prediction but also for drug design.

Discovery of 4,6- and 5,7-Disubstituted Isoquinoline Derivatives as a Novel Class of Protein Kinase C ζ Inhibitors with Fragment-Merging Strategy.

Two chemical series of novel protein kinase C ζ (PKCζ) inhibitors, 4,6-disubstituted and 5,7-disubstituted isoquinolines, were rapidly identified using our fragment merging strategy. This methodology involves biochemical screening of a high concentration of a monosubstituted isoquinoline fragment library, then merging hit isoquinoline fragments into a single compound. Our strategy can be applied to the discovery of other challenging kinase inhibitors without protein-ligand structural information. Furthermore, our optimization effort identified the highly potent and orally available 5,7-isoquinoline 37 from the second chemical series. Compound 37 showed good efficacy in a mouse collagen-induced arthritis model. The in vivo studies suggest that PKCζ inhibition is a novel target for rheumatoid arthritis (RA) and that 5,7-disubstituted isoquinoline 37 has the potential to elucidate the biological consequences of PKCζ inhibition, specifically in terms of therapeutic intervention for RA.